Faculty Bibliography

BACKGROUND:p.G12C-mutated advanced solid tumors in a phase 1 study, and particularly promising anticancer activity was observed in a subgroup of patients with non-small-cell lung cancer (NSCLC). METHODS:p.G12C-mutated advanced NSCLC previously treated with standard therapies. The primary end point was objective response (complete or partial response) according to independent central review. Key secondary end points included duration of response, disease control (defined as complete response, partial response, or stable disease), progression-free survival, overall survival, and safety. Exploratory biomarkers were evaluated for their association with response to sotorasib therapy. RESULTS:. CONCLUSIONS:p.G12C-mutated NSCLC. (Funded by Amgen and the National Institutes of Health; CodeBreaK100 ClinicalTrials.gov number, NCT03600883.).

Evidence from real-world clinical settings is lacking with regard to first-line immunotherapy plus chemotherapy for the treatment of non-small cell lung cancer (NSCLC). Our aim was to describe outcomes for patients treated with first-line pembrolizumab-combination therapy for metastatic nonsquamous NSCLC in US oncology practices. Using an anonymized, nationwide electronic health record-derived database, we identified patients who initiated pembrolizumab plus pemetrexed-carboplatin in the first-line setting (May 2017 to August 2018) after diagnosis of metastatic nonsquamous NSCLC that tested negative for EGFR and ALK genomic aberrations. Eligible patients had ECOG performance status of 0-1. An enhanced manual chart review was used to collect outcome information. Time-to-event analyses were performed using the Kaplan-Meier method. Of 283 eligible patients, 168 (59%) were male; median age was 66 years (range 33-84); and the proportions of patients with PD-L1 tumor proportion score (TPS) of ≥ 50%, 1-49%, < 1%, and unknown were 28%, 27%, 28%, and 17%, respectively. At data cutoff on August 31, 2019, median patient follow-up was 20.3 months (range 12-28 months), and median real-world times on treatment (rwToT) with pembrolizumab and pemetrexed were 5.6 (95% CI 4.5-6.4) and 2.8 months (95% CI 2.2-3.5), respectively. Median overall survival (OS) was 16.5 months (95% CI 13.2-20.6); estimated 12-month survival was 59.5% (95% CI 53.3-65.0); rwProgression-free survival was 6.4 months (95% CI 5.4-7.8); and rwTumor response rate (complete or partial response) was 56.5% (95% CI 50.5-62.4). Median OS was 20.6, 16.3, 13.2, and 13.7 months for patient cohorts with PD-L1 TPS ≥ 50%, 1-49%, < 1%, and unknown, respectively. These findings demonstrate the effectiveness of pembrolizumab plus pemetrexed-carboplatin by describing clinical outcomes among patients with metastatic nonsquamous NSCLC who were treated at US oncology practices.

Introduction: At present, there is no accurate and validated way to predict which patients would have disease recurrence following definitive therapy in ES-NSCLC patients. NSCLC mortality and recurrence risk has recently been shown to be different among different genders. In this project, in addition to creating a unified Radiomic based model, we have developed and validated Gender-Specific Radiomics models which can better to predict Disease free survival (DFS) in ES-NSCLC.
Method(s): This study comprised a total of 312 ES-NSCLC patients from 3 different institutions. A total of 757 intratumoral and peritumoral radiomic textural features were extracted from a pre-treatment diagnostic non-contrast CT scan for every patient. The three models were constructed using training cohort D1- Mall for all combined all patients(N=173), MM for Male population-specific model(N=83), and MF for model specific to Females(N=89) using the most stable, significant and uncorrelated features. Based on these three models, the three Radiomic Risk Scores were constructed using a Lasso-regularized multivariate Cox-regression model. The patients were divided into High and Low-risk groups using an optimal threshold, giving maximum hazard ratio(HR) within the training cohorts. The models were validated and compared within each other using DVAL(D 2+D3).
Result(s): All three models included three features (Table-1). The MALL could not predict DFS within any specific gender subtype but had HR of 2.17 [1.15-4.08] for the entire DVAL. The MM model explicitly constructed for the male population had HR of 2.84 [1.05-7.70] within the male-specific DVAL, increasing it by ~30.87% over overall HR. Similarly, the MF model constructed specifically for females increased the HR to 12.76[2.36-68.9] in the DVAL within specific Female population. [Formula presented] [Formula presented]
Conclusion(s): Gender-specific Radiomics based models are better at predicting DFS in ES-NSCLC than radiomic models which do not explicitly account for gender. These might be capturing the underlying differences in tumor biology and characteristics between males and females. Keywords: Gender-Specific, Radiomics, CT-Scans
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OBJECTIVE:To identify stable and discriminating radiomic features on non-contrast CT scans to develop more generalisable radiomic classifiers for distinguishing granulomas from adenocarcinomas. METHODS:). To mitigate the variation of CT acquisition parameters, we defined 'stable' radiomic features as those for which the feature expression remains relatively unchanged between different sites, as assessed using a Wilcoxon rank-sum test. These stable features were used to develop more generalisable radiomic classifiers that were more resilient to variations in lung CT scans. Features were ranked based on two criteria, firstly based on discriminability (i.e. maximising AUC) alone and subsequently based on maximising both feature stability and discriminability. Different machine-learning classifiers (Linear discriminant analysis, Quadratic discriminant analysis, Support vector machines and random forest) were trained with features selected using the two different criteria and then compared on the two independent test sets for distinguishing granulomas from adenocarcinomas, in terms of area under the receiver operating characteristic curve. RESULTS:[n = 62]: maximum AUCs of 0.87 versus. 0.79; p-value = 0.021). These differences held for features extracted from scans with <3 mm slice thickness (AUC = 0.88 versus. 0.80; p-value = 0.039, n = 100) and for the ≥3 mm cases (AUC = 0.81 versus. 0.76; p-value = 0.034, n = 105). In both experiments, shape and peritumoural texture features had a higher stability compared with intratumoural texture features. CONCLUSIONS:Our study suggests that explicitly accounting for both stability and discriminability results in more generalisable radiomic classifiers to distinguish adenocarcinomas from granulomas on non-contrast CT scans. Our results also showed that peritumoural texture and shape features were less affected by the scanner parameters compared with intratumoural texture features; however, they were also less discriminating compared with intratumoural features.

INTRODUCTION/BACKGROUND:Thromboembolism (TE) is common in patients with non-small cell lung cancer (NSCLC) and is associated with worse outcomes. Recent advances in the understanding of NSCLC have led to the identification of molecular subtypes such as ALK and EGFR mutations. The association of these subtypes with risk of TE has not been fully explored. METHODS:We conducted a retrospective cohort study of consecutive NSCLC patients seen at the Cleveland Clinic from July 2002 through July 2017 for whom molecular classification and follow-up were available. TE events included deep-vein thrombosis (DVT), pulmonary embolism (PE), visceral vein thrombosis (VVT) and arterial events. TE-free survival and overall survival rates for each of the molecular subtype (wild-type, ALK-mutant and EGFR-mutant) were estimated by the Kaplan-Meier method. Cox proportional hazard regression analysis was used to identify factors associated with the endpoints TE and overall survival. TE was analyzed as a conditional, time-dependent covariate to assess its impact with respect to overall survival. RESULTS:The study population consisted of 461 patients. Approximately half were females (n=263, 57%) and 58% (n=270) were older than 65 years. TE occurred in 98 of 461 patients (21.3%) during a median follow-up of 33.1 months. The highest cumulative rates of TE were observed in patients with ALK-mutant NSCLC (N=20/46, 43.5%) followed by patients with EGFR-mutant cancers (N=35/165, 21.2%) and wild-type cancers (N=43/250, 17.2%) p<0.05. Cumulative incidence of TE at six months of follow-up was 15.7% (95% CI: 5.0-26.4%) for ALK-mutant cancers, 8.8% (95% CI: 4.4-13.2%) for EGFR-mutant cancers, and 9.2% (95% CI: 5.4-12.9%) for wild-type cancers. Patients who experienced TE had worse overall survival [all patients: HR=2.8 95% CI 2.1-3.6, p<0.001]. CONCLUSIONS:Patients with ALK-mutant advanced lung adenocarcinoma have the highest rate of TE. TE is associated with worse survival across molecular subtypes. These findings should be taken into consideration in decision-making regarding thromboprophylaxis.

BACKGROUND:Previous studies have indicated Coronavirus disease 2019 (COVID-19) patients with cancer have a high fatality rate. METHODS:We conducted a systematic review of studies that reported fatalities in COVID-19 patients with cancer. A comprehensive meta-analysis that assessed the overall case fatality rate and associated risk factors was performed. Using individual patient data, univariate and multivariate logistic regression analyses were used to estimate odds ratios (OR) for each variable with outcomes. RESULTS:We included 15 studies with 3019 patients, of which 1628 were men; 41.0% were from the UK and Europe, followed by the USA and Canada (35.7%) and Asia (China, 23.3%). The overall case fatality rate of COVID-19 patients with cancer measured 22.4% (95% confidence interval [CI] = 17.3% to 28.0%). Univariate analysis revealed age (odds ratio [OR] = 3.57; 95% CI = 1.80 to 7.06), male (OR = 2.10; 95% CI = 1.07 to 4.13), and comorbidity (OR = 2.00; 95% CI = 1.04 to 3.85) were associated with increased risk of severe events (defined as the individuals being admitted to the intensive care unit, or requiring invasive ventilation, or death). In multivariate analysis, only age greater than 65 years (OR = 3.16; 95% CI = 1.45 to 6.88) and being male (OR = 2.29; 95% CI = 1.07 to 4.87) were associated with increased risk of severe events. CONCLUSION/CONCLUSIONS:Our analysis demonstrated that COVID-19 patients with cancer have a higher fatality rate when compared with that of COVID-19 patients without cancer. Age and gender appear to be risk factors associated with a poorer prognosis.

BACKGROUND:Use of adjuvant chemotherapy in patients with early-stage lung cancer is controversial because no definite biomarker exists to identify patients who would receive added benefit from it. We aimed to develop and validate a quantitative radiomic risk score (QuRiS) and associated nomogram (QuRNom) for early-stage non-small cell lung cancer (NSCLC) that is prognostic of disease-free survival and predictive of the added benefit of adjuvant chemotherapy following surgery. METHODS:. FINDINGS:). INTERPRETATION:QuRiS and QuRNom were validated as being prognostic of disease-free survival and predictive of the added benefit of adjuvant chemotherapy, especially in clinically defined low-risk groups. Since QuRiS is based on routine chest CT imaging, with additional multisite independent validation it could potentially be employed for decision management in non-invasive treatment of resectable lung cancer. FUNDING:National Cancer Institute of the US National Institutes of Health, National Center for Research Resources, US Department of Veterans Affairs Biomedical Laboratory Research and Development Service, Department of Defence, National Institute of Diabetes and Digestive and Kidney Diseases, Wallace H Coulter Foundation, Case Western Reserve University, and Dana Foundation.

Summary/: Background:Development and validation of a quantitative radiomic risk score (QuRiS) and associated nomogram (QuRNom) for early-stage non-small cell lung cancer (ES-NSCLC) that is prognostic of disease-free survival (DFS) and predictive of the added benefit of adjuvant chemotherapy (ACT) following surgery. Methods:. Findings:,p<0·05, N=86) and other immune specific biological pathways. Interpretation:QuRiS and QuRNom were validated as being prognostic of DFS and predictive of the added benefit of ACT.

Local spatial arrangement of nuclei in histopathology images of different cancer subtypes has been shown to have prognostic value. In order to capture localized nuclear architectural information, local cell cluster graph-based measurements have been proposed. However, conventional ways of cell graph construction only utilize nuclear spatial proximity, and do not differentiate between different cell types while constructing the graph. In this paper, we present feature-driven local cell cluster graph (FLocK), a new approach to constructing local cell graphs by simultaneously considering spatial proximity and attributes of the individual nuclei (e.g. shape, size, texture). In addition, we have designed a new set of quantitative graph-derived metrics to be extracted from FLocKs, in turn capturing the interplay between different proximally located clusters of nuclei. We have evaluated the efficacy of FLocK features extracted from H&E stained tissue images in two clinical applications: to classify short-term vs. long-term survival among patients of early stage non-small cell lung cancer (ES-NSCLC), and also to predict human papillomavirus (HPV) status of oropharyngeal squamous cell carcinoma (OP-SCCs). In the classification of long-term vs. short-term survival among patients of ES-NSCLC (training cohort, n = 434), the top 10 discriminative FLocK features related to the variation of FLocK size and intersected FLocK distance were identified, via Minimum Redundancy and Maximum Relevance (MRMR) selection, in 100 runs of 10-fold cross-validation, and in conjunction with a linear discriminant classifier yielded a mean AUC of 0.68 for predicting survival in the training cohort. This is better than other state-of-art histomorphometric and deep learning classifiers (cell cluster graphs (AUC = 0.62), global cell graph (AUC = 0.56), nuclear shape (AUC = 0.54), nuclear orientation (AUC = 0.61), AlexNet (AUC = 0.55), ResNet (AUC = 0.56)). The FLocK-based classifier yielded an AUC of 0.70 in an independent testing cohort (n = 150). The patients identified as "high-risk" had significantly poorer overall survival in the testing cohort, with a hazard ratio (95% confidence interval) of 2.24 (1.24-4.05), p = 0.01144). In the classification of HPV status of OP-SCC, the top three FLocK features pertaining to the portion of intersected FLocKs were used to construct a classifier, which yielded an AUC of 0.80 in the training cohort (n = 50), and an accuracy of 0.78 in an independent testing cohort (n = 35). The combination of FLocK measurements with cell cluster graphs, nuclear orientation, and nuclear shape improved the training AUC to 0.87, 0.91 and 0.85, respectively. Deep learning approaches yielded marginally better performance than the FLocK-based classifier in this application, with AUC = 0.78 for AlexNet, AUC = 0.81 for ResNet, and AUC = 0.76 for FLocK-based classifier in the testing cohort. However, the combination of two hand-crafted features: FLocK and nuclear orientation yielded a better performance (AUC = 0.84). FLocK provides a unique and quantitative way to analyze histology images of solid tumors and interrogate tumor morphology from a different aspect than existing histomorphometrics. The source code can be accessed at https://github.com/hacylu/FLocK.